Category: Data Science
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GPT-5: Peak or Plateau? A Literature Review of Progress in Large Language Models
Is GPT-5 a revolutionary step toward greater AI intelligence, or a sign of diminishing returns in scaling large language models? This work presents a comprehensive analysis of literature and reports from the last several years to answer that question. We review the development from GPT-3 through GPT-4 and into GPT-5, highlighting how earlier leaps in…
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“Compute as Teacher”: How AI Models Learn by Teaching Themselves
One of the big challenges in training AI language models is the need for high-quality supervision — in other words, showing the model what the “right” answers look like. Traditionally, this is done with large datasets of human-written answers or by having humans provide feedback on the AI’s outputs (as in Reinforcement Learning from Human Feedback, RLHF).…
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Tailoring AI Behavior to Context: Adaptive HHH Alignment
Ensuring that AI assistants are Helpful, Honest, and Harmless (HHH) has become a standard goal in language model alignment. These three principles, popularized by OpenAI and others, serve as a north star for “good” AI behavior: a system should effectively help the user, be truthful in what it says, and avoid causing harm (e.g. through offensive or…
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Qwen3-Next 80B: A New Generation of Efficient Large Language Model
Qwen3-Next-80B is a recently unveiled 80-billion-parameter large language model that achieves high performance through an innovative sparse architecture. Developed as part of Alibaba’s Qwen series, Qwen3-Next-80B stands out by activating only a small fraction of its parameters for each token generation — dramatically reducing computation while preserving capability. In practical terms, it matches or even exceeds the performance of…